利用RFM模型挖掘有价值的模糊模式

Yanlin Qi, Fuyin Lai, Guoting Chen, Wensheng Gan
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引用次数: 1

摘要

本文旨在将模糊方法应用于RFM模型,提出一种有效的算法来发现有价值的模式。RFM分析是客户关系管理中常用的一种方法,通过RFM分析可以识别出有价值的客户群体。通过将RFM分析与频繁的模式挖掘相结合,可以从RFM模式树中发现有价值的RFM模式,例如RFM增长算法。为了挖掘项目间具有定量关系的模式,在RFM模型中引入模糊方法,提出了一种模糊- Rfu -树算法,该算法提出了一种新的剪剪策略来剪剪候选模式。实验证明了新算法的有效性。新算法保证了与由rfm生长生成的rfm模式的高度重叠,挖掘的模式中有更多有价值的信息(增加了额外的模糊级别)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mining Valuable Fuzzy Patterns via the RFM Model
This paper aims to propose an effective algorithm to discover valuable patterns by applying the fuzzy method to the RFM model. RFM analysis is a common method in customer relationship management, through which we can identify valuable customer groups. By combining RFM analysis with frequent pattern mining, valuable RFM - patterns can be found from the RFM-pattern-tree, such as the RFMP-growth algorithm. Aiming to mine patterns that have quantitative relationships among items, we introduce the fuzzy method in the RFM model, and we present a fuzzy - Rfu - tree algorithm in which a new pruning strategy is proposed to prune candidate patterns. Experiments show the effectiveness of the new algorithm. The new algorithm guarantees a high overlap degree with the RFM-patterns gen-erated by RFMP-growth, with more valuable information (with additional fuzzy level) in the mined patterns.
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